AI-Driven Multilingual Framework for Engineering Education and Knowledge Transfer in Non-English-Speaking Regions

English-language dominance remains a significant barrier to global participation in engineering education, innovation, and professional knowledge exchange. Many experienced engineers and practitioners in non-English-speaking regions are unable to fully engage in international research communities due to linguistic constraints, despite possessing valuable technical expertise and practical knowledge. This working paper proposes a practical framework for AI-driven multilingual engineering education designed to reduce language barriers and promote inclusive knowledge transfer. By leveraging large language models, automated translation, and AI-assisted content generation, the framework enables engineers, educators, and learners to access, create, and share technical knowledge in multiple languages. Rather than focusing on theoretical optimization, this study emphasizes real-world implementation and applicability. The framework is intended for use in engineering education, professional training, and cross-border collaboration, particularly in regions where English is not the primary working language. The paper argues that AI-assisted multilingual systems can preserve expert knowledge, expand access to global engineering discourse, and accelerate innovation by enabling broader participation in international technical communities.